Overview

Dataset statistics

Number of variables24
Number of observations848411
Missing cells0
Missing cells (%)0.0%
Duplicate rows2442
Duplicate rows (%)0.3%
Total size in memory113.3 MiB
Average record size in memory140.0 B

Variable types

DateTime1
Numeric22
Categorical1

Alerts

Dataset has 2442 (0.3%) duplicate rowsDuplicates
RoomTemperature is highly correlated with RelativeHumiditySupplyAir and 1 other fieldsHigh correlation
HeaterPerc is highly correlated with HeatingPowerHigh correlation
CoolerPerc is highly correlated with CoolingPowerHigh correlation
TempSupplyAir is highly correlated with RelativeHumiditySupplyAir and 1 other fieldsHigh correlation
RelativeHumiditySupplyAir is highly correlated with RoomTemperature and 3 other fieldsHigh correlation
HeatingPower is highly correlated with HeaterPerc and 1 other fieldsHigh correlation
CoolingPower is highly correlated with CoolerPercHigh correlation
AirTemperature is highly correlated with RoomTemperature and 4 other fieldsHigh correlation
BrightnessNorth is highly correlated with AirTemperature and 5 other fieldsHigh correlation
BrightnessEast is highly correlated with BrightnessNorth and 4 other fieldsHigh correlation
BrightnessSouth is highly correlated with BrightnessNorth and 4 other fieldsHigh correlation
BrightnessWest is highly correlated with AirTemperature and 5 other fieldsHigh correlation
dwpt is highly correlated with RelativeHumiditySupplyAir and 1 other fieldsHigh correlation
rhum is highly correlated with BrightnessNorth and 4 other fieldsHigh correlation
tsun is highly correlated with BrightnessNorth and 4 other fieldsHigh correlation
RoomTemperature is highly correlated with RelativeHumiditySupplyAirHigh correlation
HeaterPerc is highly correlated with HeatingPowerHigh correlation
TempSupplyAir is highly correlated with RelativeHumiditySupplyAir and 1 other fieldsHigh correlation
RelativeHumiditySupplyAir is highly correlated with RoomTemperature and 3 other fieldsHigh correlation
HeatingPower is highly correlated with HeaterPerc and 1 other fieldsHigh correlation
AirTemperature is highly correlated with RelativeHumiditySupplyAir and 4 other fieldsHigh correlation
BrightnessNorth is highly correlated with AirTemperature and 5 other fieldsHigh correlation
BrightnessEast is highly correlated with BrightnessNorth and 3 other fieldsHigh correlation
BrightnessSouth is highly correlated with BrightnessNorth and 4 other fieldsHigh correlation
BrightnessWest is highly correlated with AirTemperature and 4 other fieldsHigh correlation
dwpt is highly correlated with RelativeHumiditySupplyAir and 1 other fieldsHigh correlation
rhum is highly correlated with AirTemperature and 5 other fieldsHigh correlation
tsun is highly correlated with BrightnessNorth and 4 other fieldsHigh correlation
HeaterPerc is highly correlated with HeatingPowerHigh correlation
CoolerPerc is highly correlated with CoolingPowerHigh correlation
TempSupplyAir is highly correlated with RelativeHumiditySupplyAirHigh correlation
RelativeHumiditySupplyAir is highly correlated with TempSupplyAirHigh correlation
HeatingPower is highly correlated with HeaterPercHigh correlation
CoolingPower is highly correlated with CoolerPercHigh correlation
AirTemperature is highly correlated with dwptHigh correlation
BrightnessNorth is highly correlated with BrightnessEast and 3 other fieldsHigh correlation
BrightnessEast is highly correlated with BrightnessNorth and 3 other fieldsHigh correlation
BrightnessSouth is highly correlated with BrightnessNorth and 4 other fieldsHigh correlation
BrightnessWest is highly correlated with BrightnessNorth and 3 other fieldsHigh correlation
dwpt is highly correlated with AirTemperatureHigh correlation
rhum is highly correlated with BrightnessSouthHigh correlation
tsun is highly correlated with BrightnessNorth and 3 other fieldsHigh correlation
HeaterPerc is highly correlated with HeatingPowerHigh correlation
CoolerPerc is highly correlated with CoolingPowerHigh correlation
TempSupplyAir is highly correlated with RelativeHumiditySupplyAirHigh correlation
RelativeHumiditySupplyAir is highly correlated with TempSupplyAirHigh correlation
HeatingPower is highly correlated with HeaterPercHigh correlation
CoolingPower is highly correlated with CoolerPercHigh correlation
AirTemperature is highly correlated with BrightnessNorth and 4 other fieldsHigh correlation
BrightnessNorth is highly correlated with AirTemperature and 5 other fieldsHigh correlation
BrightnessEast is highly correlated with BrightnessNorth and 4 other fieldsHigh correlation
BrightnessSouth is highly correlated with AirTemperature and 5 other fieldsHigh correlation
BrightnessWest is highly correlated with AirTemperature and 5 other fieldsHigh correlation
dwpt is highly correlated with AirTemperatureHigh correlation
rhum is highly correlated with AirTemperature and 5 other fieldsHigh correlation
tsun is highly correlated with BrightnessNorth and 4 other fieldsHigh correlation
Room has 142676 (16.8%) zeros Zeros
AirqualityPerc has 160732 (18.9%) zeros Zeros
HeaterPerc has 497718 (58.7%) zeros Zeros
CoolerPerc has 684100 (80.6%) zeros Zeros
HeatingPower has 635438 (74.9%) zeros Zeros
CoolingPower has 754961 (89.0%) zeros Zeros
WindDirection has 304105 (35.8%) zeros Zeros
BrightnessNorth has 414876 (48.9%) zeros Zeros
BrightnessEast has 414388 (48.8%) zeros Zeros
BrightnessSouth has 415732 (49.0%) zeros Zeros
BrightnessWest has 413508 (48.7%) zeros Zeros
BucketAttendees has 818422 (96.5%) zeros Zeros
prcp has 734300 (86.6%) zeros Zeros
tsun has 568344 (67.0%) zeros Zeros
coco has 15378 (1.8%) zeros Zeros

Reproduction

Analysis started2022-05-07 10:29:56.760841
Analysis finished2022-05-07 10:35:32.280584
Duration5 minutes and 35.52 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Distinct140231
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
Minimum2018-01-01 00:00:00
Maximum2021-12-31 23:45:00
2022-05-07T12:35:32.493580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:32.745097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Room
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.496415063
Minimum0
Maximum5
Zeros142676
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size6.5 MiB
2022-05-07T12:35:32.932146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.709282362
Coefficient of variation (CV)0.6846947798
Kurtosis-1.269891184
Mean2.496415063
Median Absolute Deviation (MAD)2
Skewness0.0007064616374
Sum2117986
Variance2.921646193
MonotonicityNot monotonic
2022-05-07T12:35:33.092939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0142676
16.8%
3141685
16.7%
4141232
16.6%
5141073
16.6%
2140893
16.6%
1140852
16.6%
ValueCountFrequency (%)
0142676
16.8%
1140852
16.6%
2140893
16.6%
3141685
16.7%
4141232
16.6%
5141073
16.6%
ValueCountFrequency (%)
5141073
16.6%
4141232
16.6%
3141685
16.7%
2140893
16.6%
1140852
16.6%
0142676
16.8%

RoomTemperature
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct140
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.16507624
Minimum12
Maximum27.60000038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-05-07T12:35:33.556777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile20.10000038
Q121.60000038
median22.29999924
Q322.89999962
95-th percentile23.70000076
Maximum27.60000038
Range15.60000038
Interquartile range (IQR)1.299999237

Descriptive statistics

Standard deviation1.125211
Coefficient of variation (CV)0.05076504084
Kurtosis1.827415943
Mean22.16507624
Median Absolute Deviation (MAD)0.6000003815
Skewness-0.8915127516
Sum18805094.5
Variance1.266099691
MonotonicityNot monotonic
2022-05-07T12:35:33.787292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.6000003840454
 
4.8%
22.7000007638621
 
4.6%
22.7999992438426
 
4.5%
21.8999996236934
 
4.4%
22.535537
 
4.2%
22.3999996234150
 
4.0%
22.2000007631962
 
3.8%
22.8999996231417
 
3.7%
22.2999992431306
 
3.7%
2331054
 
3.7%
Other values (130)498550
58.8%
ValueCountFrequency (%)
121
< 0.1%
12.199999812
< 0.1%
12.600000381
< 0.1%
12.800000191
< 0.1%
12.899999621
< 0.1%
131
< 0.1%
13.100000381
< 0.1%
13.300000192
< 0.1%
13.51
< 0.1%
13.800000191
< 0.1%
ValueCountFrequency (%)
27.600000382
 
< 0.1%
27.52
 
< 0.1%
27.3999996210
< 0.1%
27.299999245
 
< 0.1%
27.200000765
 
< 0.1%
27.100000383
 
< 0.1%
275
 
< 0.1%
26.8999996213
< 0.1%
26.799999248
< 0.1%
26.700000768
< 0.1%

AirqualityPerc
Real number (ℝ≥0)

ZEROS

Distinct450
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.751688389
Minimum0
Maximum55.29999924
Zeros160732
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-05-07T12:35:34.035121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.899999976
median9.600000381
Q316.39999962
95-th percentile20.5
Maximum55.29999924
Range55.29999924
Interquartile range (IQR)14.49999964

Descriptive statistics

Standard deviation7.78826046
Coefficient of variation (CV)0.7986576426
Kurtosis-0.7705032229
Mean9.751688389
Median Absolute Deviation (MAD)7.200000286
Skewness0.3153251112
Sum8273439.698
Variance60.65699768
MonotonicityNot monotonic
2022-05-07T12:35:34.235650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0160732
 
18.9%
19.2999992427266
 
3.2%
19.2000007622273
 
2.6%
19.1000003817185
 
2.0%
19.3999996214324
 
1.7%
19.59727
 
1.1%
14.53723
 
0.4%
13.800000193701
 
0.4%
14.100000383675
 
0.4%
14.600000383603
 
0.4%
Other values (440)582202
68.6%
ValueCountFrequency (%)
0160732
18.9%
0.10000000152498
 
0.3%
0.2000000032427
 
0.3%
0.30000001192687
 
0.3%
0.4000000062686
 
0.3%
0.52508
 
0.3%
0.60000002382592
 
0.3%
0.69999998812658
 
0.3%
0.80000001192760
 
0.3%
0.89999997622697
 
0.3%
ValueCountFrequency (%)
55.299999241
< 0.1%
551
< 0.1%
54.700000761
< 0.1%
531
< 0.1%
49.700000761
< 0.1%
49.599998471
< 0.1%
481
< 0.1%
47.900001531
< 0.1%
471
< 0.1%
46.799999241
< 0.1%

HeaterPerc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct765
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.180841584
Minimum0
Maximum100
Zeros497718
Zeros (%)58.7%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-05-07T12:35:34.470445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.700000048
95-th percentile29
Maximum100
Range100
Interquartile range (IQR)1.700000048

Descriptive statistics

Standard deviation12.53284073
Coefficient of variation (CV)2.997683714
Kurtosis23.16786766
Mean4.180841584
Median Absolute Deviation (MAD)0
Skewness4.499892235
Sum3547071.989
Variance157.0720978
MonotonicityNot monotonic
2022-05-07T12:35:34.710453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0497718
58.7%
1.60000002458714
 
6.9%
1.70000004851858
 
6.1%
1.79999995228353
 
3.3%
1.519770
 
2.3%
0.899999976212528
 
1.5%
1.39999997611394
 
1.3%
0.80000001199048
 
1.1%
1.2999999526645
 
0.8%
2.5999999055554
 
0.7%
Other values (755)146829
 
17.3%
ValueCountFrequency (%)
0497718
58.7%
0.1000000015396
 
< 0.1%
0.20000000360
 
< 0.1%
0.300000011936
 
< 0.1%
0.40000000631
 
< 0.1%
0.530
 
< 0.1%
0.600000023896
 
< 0.1%
0.6999999881948
 
0.1%
0.80000001199048
 
1.1%
0.899999976212528
 
1.5%
ValueCountFrequency (%)
10048
 
< 0.1%
99.900001536
 
< 0.1%
99.8000030566
 
< 0.1%
99.699996952511
0.3%
99.59999847811
 
0.1%
98.800003056
 
< 0.1%
98.199996956
 
< 0.1%
98.0999984712
 
< 0.1%
9812
 
< 0.1%
97.900001536
 
< 0.1%

CoolerPerc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct477
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.029707539
Minimum0
Maximum100
Zeros684100
Zeros (%)80.6%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-05-07T12:35:34.957872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.599999905
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.713343143
Coefficient of variation (CV)6.519660088
Kurtosis148.5233307
Mean1.029707539
Median Absolute Deviation (MAD)0
Skewness11.40591812
Sum873615.2028
Variance45.06897354
MonotonicityNot monotonic
2022-05-07T12:35:35.178228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0684100
80.6%
0.699999988134009
 
4.0%
0.600000023831195
 
3.7%
0.800000011919414
 
2.3%
0.518097
 
2.1%
0.89999997624782
 
0.6%
3.5999999053711
 
0.4%
2.53576
 
0.4%
1.7000000482651
 
0.3%
2.5999999052371
 
0.3%
Other values (467)44505
 
5.2%
ValueCountFrequency (%)
0684100
80.6%
0.100000001545
 
< 0.1%
0.20000000342
 
< 0.1%
0.300000011966
 
< 0.1%
0.400000006884
 
0.1%
0.518097
 
2.1%
0.600000023831195
 
3.7%
0.699999988134009
 
4.0%
0.800000011919414
 
2.3%
0.89999997624782
 
0.6%
ValueCountFrequency (%)
10048
 
< 0.1%
99.90000153354
 
< 0.1%
99.800003051242
0.1%
99.69999695264
 
< 0.1%
97.6999969524
 
< 0.1%
97.5999984760
 
< 0.1%
97.524
 
< 0.1%
97.4000015312
 
< 0.1%
97.300003056
 
< 0.1%
96.5999984736
 
< 0.1%

TempSupplyAir
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1098
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.29385293
Minimum0
Maximum50
Zeros174
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-05-07T12:35:35.424920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.20999908
Q119.27000046
median20.27000046
Q321.15999985
95-th percentile22.63999939
Maximum50
Range50
Interquartile range (IQR)1.88999939

Descriptive statistics

Standard deviation1.480688214
Coefficient of variation (CV)0.07296239997
Kurtosis14.6123209
Mean20.29385293
Median Absolute Deviation (MAD)0.9599990845
Skewness0.0008916640072
Sum17217528.06
Variance2.192437649
MonotonicityNot monotonic
2022-05-07T12:35:35.652981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.2099990814299
 
1.7%
19.4099998512060
 
1.4%
18.9400005311749
 
1.4%
19.6800003110793
 
1.3%
20.010000239961
 
1.2%
20.610000619846
 
1.2%
20.540000929619
 
1.1%
20.879999169567
 
1.1%
19.010000239115
 
1.1%
20.759006
 
1.1%
Other values (1088)742396
87.5%
ValueCountFrequency (%)
0174
< 0.1%
6.6100001346
 
< 0.1%
10.609999666
 
< 0.1%
10.649999626
 
< 0.1%
11.869999896
 
< 0.1%
12.600000386
 
< 0.1%
12.810000426
 
< 0.1%
136
 
< 0.1%
13.010000236
 
< 0.1%
13.170000086
 
< 0.1%
ValueCountFrequency (%)
5036
< 0.1%
34.729999546
 
< 0.1%
34.490001686
 
< 0.1%
28.280000696
 
< 0.1%
28.229999546
 
< 0.1%
28.219999316
 
< 0.1%
28.180000316
 
< 0.1%
28.100000386
 
< 0.1%
28.0200004612
 
< 0.1%
27.889999396
 
< 0.1%

RelativeHumiditySupplyAir
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct204
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.73846614
Minimum0
Maximum100
Zeros138
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-05-07T12:35:35.836325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.79999924
Q141.40000153
median43.29999924
Q344.29999924
95-th percentile45.29999924
Maximum100
Range100
Interquartile range (IQR)2.899997711

Descriptive statistics

Standard deviation2.265540123
Coefficient of variation (CV)0.05300939242
Kurtosis55.27735138
Mean42.73846614
Median Absolute Deviation (MAD)1.299999237
Skewness-0.3923251331
Sum36259784.8
Variance5.132671833
MonotonicityNot monotonic
2022-05-07T12:35:36.061047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4433471
 
3.9%
44.4000015327173
 
3.2%
43.9000015326899
 
3.2%
43.523695
 
2.8%
44.523545
 
2.8%
43.5999984723204
 
2.7%
44.2999992422621
 
2.7%
44.7999992421526
 
2.5%
44.7000007619978
 
2.4%
43.7000007619757
 
2.3%
Other values (194)606542
71.5%
ValueCountFrequency (%)
0138
< 0.1%
25.512
 
< 0.1%
25.700000766
 
< 0.1%
2612
 
< 0.1%
26.1000003812
 
< 0.1%
26.3999996212
 
< 0.1%
26.56
 
< 0.1%
26.7999992412
 
< 0.1%
27.100000386
 
< 0.1%
27.299999246
 
< 0.1%
ValueCountFrequency (%)
10072
< 0.1%
51.900001536
 
< 0.1%
51.4000015372
< 0.1%
51.2000007642
< 0.1%
51.0999984724
 
< 0.1%
5112
 
< 0.1%
50.799999246
 
< 0.1%
50.7000007612
 
< 0.1%
50.599998476
 
< 0.1%
50.512
 
< 0.1%

HeatingPower
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3425
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.48559189
Minimum-4.699999809
Maximum506.7000122
Zeros635438
Zeros (%)74.9%
Negative7735
Negative (%)0.9%
Memory size3.2 MiB
2022-05-07T12:35:36.288544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-4.699999809
5-th percentile0
Q10
median0
Q30
95-th percentile136.5
Maximum506.7000122
Range511.400012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation57.27389908
Coefficient of variation (CV)2.162454942
Kurtosis9.612288475
Mean26.48559189
Median Absolute Deviation (MAD)0
Skewness2.808483601
Sum22470667.5
Variance3280.299316
MonotonicityNot monotonic
2022-05-07T12:35:36.508520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0635438
74.9%
0.10000000151010
 
0.1%
66.40000153524
 
0.1%
76.40000153524
 
0.1%
65522
 
0.1%
77.19999695512
 
0.1%
76.59999847493
 
0.1%
-4.300000191492
 
0.1%
71.40000153486
 
0.1%
77.5475
 
0.1%
Other values (3415)207935
 
24.5%
ValueCountFrequency (%)
-4.69999980924
 
< 0.1%
-4.59999990596
 
< 0.1%
-4.5312
< 0.1%
-4.400000095462
0.1%
-4.300000191492
0.1%
-4.199999809432
0.1%
-4.099999905282
< 0.1%
-4246
< 0.1%
-3.900000095264
< 0.1%
-3.799999952282
< 0.1%
ValueCountFrequency (%)
506.70001226
< 0.1%
458.39999396
< 0.1%
4516
< 0.1%
440.20001226
< 0.1%
439.39999396
< 0.1%
438.39999396
< 0.1%
437.39999396
< 0.1%
434.799987812
< 0.1%
4346
< 0.1%
433.56
< 0.1%

CoolingPower
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct575
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.920204476
Minimum0
Maximum68.40000153
Zeros754961
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-05-07T12:35:36.728323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20.60000038
Maximum68.40000153
Range68.40000153
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.840014935
Coefficient of variation (CV)3.562128419
Kurtosis15.70216465
Mean1.920204476
Median Absolute Deviation (MAD)0
Skewness3.886308908
Sum1629122.6
Variance46.78580475
MonotonicityNot monotonic
2022-05-07T12:35:36.994496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0754961
89.0%
0.2000000034837
 
0.6%
0.30000001193837
 
0.5%
0.10000000152667
 
0.3%
0.4000000062472
 
0.3%
0.51836
 
0.2%
0.60000002381488
 
0.2%
0.69999998811016
 
0.1%
0.8000000119687
 
0.1%
23.10000038605
 
0.1%
Other values (565)74005
 
8.7%
ValueCountFrequency (%)
0754961
89.0%
0.10000000152667
 
0.3%
0.2000000034837
 
0.6%
0.30000001193837
 
0.5%
0.4000000062472
 
0.3%
0.51836
 
0.2%
0.60000002381488
 
0.2%
0.69999998811016
 
0.1%
0.8000000119687
 
0.1%
0.8999999762390
 
< 0.1%
ValueCountFrequency (%)
68.400001536
< 0.1%
686
< 0.1%
65.56
< 0.1%
65.400001536
< 0.1%
64.699996956
< 0.1%
636
< 0.1%
62.299999246
< 0.1%
626
< 0.1%
61.900001538
< 0.1%
61.700000766
< 0.1%

AirTemperature
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct563
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.6955221
Minimum-16.10000038
Maximum41.79999924
Zeros1464
Zeros (%)0.2%
Negative37524
Negative (%)4.4%
Memory size3.2 MiB
2022-05-07T12:35:37.223874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-16.10000038
5-th percentile0.3000000119
Q15.5
median10.89999962
Q317.29999924
95-th percentile26.10000038
Maximum41.79999924
Range57.89999962
Interquartile range (IQR)11.79999924

Descriptive statistics

Standard deviation8.121736526
Coefficient of variation (CV)0.6944312924
Kurtosis-0.2079547197
Mean11.6955221
Median Absolute Deviation (MAD)5.800001144
Skewness0.3211560845
Sum9922609.599
Variance65.96260071
MonotonicityNot monotonic
2022-05-07T12:35:37.414581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.6999998095655
 
0.7%
4.5999999055104
 
0.6%
4.4000000955065
 
0.6%
45033
 
0.6%
7.9000000954989
 
0.6%
3.5999999054978
 
0.6%
11.199999814880
 
0.6%
6.0999999054838
 
0.6%
6.6999998094752
 
0.6%
5.5999999054743
 
0.6%
Other values (553)798374
94.1%
ValueCountFrequency (%)
-16.100000386
 
< 0.1%
-15.899999626
 
< 0.1%
-15.8000001918
< 0.1%
-15.6999998142
< 0.1%
-15.6000003812
 
< 0.1%
-15.518
< 0.1%
-15.3999996212
 
< 0.1%
-15.3000001912
 
< 0.1%
-15.1999998112
 
< 0.1%
-15.1000003818
< 0.1%
ValueCountFrequency (%)
41.7999992412
< 0.1%
40.700000766
 
< 0.1%
40.5999984712
< 0.1%
40.400001536
 
< 0.1%
40.099998476
 
< 0.1%
39.9000015324
< 0.1%
39.7000007612
< 0.1%
39.5999984712
< 0.1%
39.56
 
< 0.1%
39.4000015312
< 0.1%

WindDirection
Real number (ℝ≥0)

ZEROS

Distinct361
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.9108864
Minimum0
Maximum360
Zeros304105
Zeros (%)35.8%
Negative0
Negative (%)0.0%
Memory size6.5 MiB
2022-05-07T12:35:37.900570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median90
Q3263
95-th percentile311
Maximum360
Range360
Interquartile range (IQR)263

Descriptive statistics

Standard deviation119.4990649
Coefficient of variation (CV)0.9490765128
Kurtosis-1.510857247
Mean125.9108864
Median Absolute Deviation (MAD)90
Skewness0.2848586636
Sum106824181
Variance14280.02652
MonotonicityNot monotonic
2022-05-07T12:35:38.085348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0304105
35.8%
270144461
17.0%
9066548
 
7.8%
18042948
 
5.1%
22521868
 
2.6%
36013047
 
1.5%
31511616
 
1.4%
455335
 
0.6%
1353818
 
0.5%
862099
 
0.2%
Other values (351)232566
27.4%
ValueCountFrequency (%)
0304105
35.8%
1175
 
< 0.1%
2277
 
< 0.1%
3150
 
< 0.1%
4150
 
< 0.1%
5222
 
< 0.1%
6240
 
< 0.1%
7204
 
< 0.1%
8193
 
< 0.1%
9150
 
< 0.1%
ValueCountFrequency (%)
36013047
1.5%
359132
 
< 0.1%
358228
 
< 0.1%
357271
 
< 0.1%
356264
 
< 0.1%
355193
 
< 0.1%
354180
 
< 0.1%
353198
 
< 0.1%
352192
 
< 0.1%
351283
 
< 0.1%

BrightnessNorth
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct469
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.861957945
Minimum0
Maximum60.09999847
Zeros414876
Zeros (%)48.9%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-05-07T12:35:38.324534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1000000015
Q36.900000095
95-th percentile23
Maximum60.09999847
Range60.09999847
Interquartile range (IQR)6.900000095

Descriptive statistics

Standard deviation8.102571487
Coefficient of variation (CV)1.666524388
Kurtosis4.848392487
Mean4.861957945
Median Absolute Deviation (MAD)0.1000000015
Skewness2.174777746
Sum4124938.602
Variance65.65166473
MonotonicityNot monotonic
2022-05-07T12:35:38.537866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0414876
48.9%
0.100000001510143
 
1.2%
0.2000000039331
 
1.1%
0.56450
 
0.8%
0.80000001195615
 
0.7%
0.4000000065568
 
0.7%
0.30000001195077
 
0.6%
14652
 
0.5%
0.69999998814532
 
0.5%
1.1000000244317
 
0.5%
Other values (459)377850
44.5%
ValueCountFrequency (%)
0414876
48.9%
0.100000001510143
 
1.2%
0.2000000039331
 
1.1%
0.30000001195077
 
0.6%
0.4000000065568
 
0.7%
0.56450
 
0.8%
0.60000002383189
 
0.4%
0.69999998814532
 
0.5%
0.80000001195615
 
0.7%
0.89999997622807
 
0.3%
ValueCountFrequency (%)
60.099998476
 
< 0.1%
55.900001536
 
< 0.1%
55.599998476
 
< 0.1%
54.5999984713
< 0.1%
53.5999984720
< 0.1%
53.299999246
 
< 0.1%
52.900001536
 
< 0.1%
52.799999246
 
< 0.1%
52.0999984712
< 0.1%
51.900001536
 
< 0.1%

BrightnessEast
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct772
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.33560939
Minimum0
Maximum145.3000031
Zeros414388
Zeros (%)48.8%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-05-07T12:35:38.741780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1000000015
Q38.5
95-th percentile67.90000153
Maximum145.3000031
Range145.3000031
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation21.67531967
Coefficient of variation (CV)2.097149655
Kurtosis7.137229443
Mean10.33560939
Median Absolute Deviation (MAD)0.1000000015
Skewness2.7415061
Sum8768844.699
Variance469.819458
MonotonicityNot monotonic
2022-05-07T12:35:38.967700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0414388
48.8%
0.100000001510310
 
1.2%
0.2000000038679
 
1.0%
0.56651
 
0.8%
0.80000001195420
 
0.6%
0.4000000065403
 
0.6%
0.30000001194922
 
0.6%
1.1000000244527
 
0.5%
1.2999999524113
 
0.5%
14095
 
0.5%
Other values (762)379903
44.8%
ValueCountFrequency (%)
0414388
48.8%
0.100000001510310
 
1.2%
0.2000000038679
 
1.0%
0.30000001194922
 
0.6%
0.4000000065403
 
0.6%
0.56651
 
0.8%
0.60000002383161
 
0.4%
0.69999998814065
 
0.5%
0.80000001195420
 
0.6%
0.89999997622743
 
0.3%
ValueCountFrequency (%)
145.30000316
< 0.1%
143.60000616
< 0.1%
142.80000316
< 0.1%
141.89999396
< 0.1%
140.60000616
< 0.1%
140.19999696
< 0.1%
139.80000316
< 0.1%
1396
< 0.1%
138.10000616
< 0.1%
137.30000316
< 0.1%

BrightnessSouth
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct784
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.62331134
Minimum0
Maximum158.3999939
Zeros415732
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-05-07T12:35:39.196291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1000000015
Q39.5
95-th percentile75.59999847
Maximum158.3999939
Range158.3999939
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation23.80919266
Coefficient of variation (CV)2.048400147
Kurtosis5.854674816
Mean11.62331134
Median Absolute Deviation (MAD)0.1000000015
Skewness2.54088974
Sum9861345.195
Variance566.8776245
MonotonicityNot monotonic
2022-05-07T12:35:39.389471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0415732
49.0%
0.100000001510508
 
1.2%
0.2000000038992
 
1.1%
0.56397
 
0.8%
0.80000001195857
 
0.7%
0.4000000065763
 
0.7%
0.30000001195089
 
0.6%
0.69999998814469
 
0.5%
1.1000000244284
 
0.5%
14260
 
0.5%
Other values (774)377060
44.4%
ValueCountFrequency (%)
0415732
49.0%
0.100000001510508
 
1.2%
0.2000000038992
 
1.1%
0.30000001195089
 
0.6%
0.4000000065763
 
0.7%
0.56397
 
0.8%
0.60000002383458
 
0.4%
0.69999998814469
 
0.5%
0.80000001195857
 
0.7%
0.89999997622943
 
0.3%
ValueCountFrequency (%)
158.399993919
< 0.1%
1586
 
< 0.1%
157.60000616
 
< 0.1%
155.89999396
 
< 0.1%
147.800003112
< 0.1%
146.10000616
 
< 0.1%
145.300003112
< 0.1%
14418
< 0.1%
142.80000316
 
< 0.1%
142.300003118
< 0.1%

BrightnessWest
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct743
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.974496199
Minimum0
Maximum147.3999939
Zeros413508
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-05-07T12:35:39.638518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.200000003
Q38.899999619
95-th percentile51.59999847
Maximum147.3999939
Range147.3999939
Interquartile range (IQR)8.899999619

Descriptive statistics

Standard deviation18.51635933
Coefficient of variation (CV)2.06321992
Kurtosis9.912195206
Mean8.974496199
Median Absolute Deviation (MAD)0.200000003
Skewness3.050058842
Sum7614061.295
Variance342.8555298
MonotonicityNot monotonic
2022-05-07T12:35:39.855402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0413508
48.7%
0.10000000159893
 
1.2%
0.2000000038063
 
1.0%
0.55945
 
0.7%
0.4000000065174
 
0.6%
0.30000001194861
 
0.6%
0.80000001194850
 
0.6%
13886
 
0.5%
1.1000000243878
 
0.5%
0.69999998813800
 
0.4%
Other values (733)384553
45.3%
ValueCountFrequency (%)
0413508
48.7%
0.10000000159893
 
1.2%
0.2000000038063
 
1.0%
0.30000001194861
 
0.6%
0.4000000065174
 
0.6%
0.55945
 
0.7%
0.60000002382910
 
0.3%
0.69999998813800
 
0.4%
0.80000001194850
 
0.6%
0.89999997622947
 
0.3%
ValueCountFrequency (%)
147.39999396
< 0.1%
143.60000616
< 0.1%
143.19999696
< 0.1%
140.600006112
< 0.1%
138.10000616
< 0.1%
137.30000316
< 0.1%
1366
< 0.1%
135.60000616
< 0.1%
1336
< 0.1%
129.69999696
< 0.1%

Beamerstate
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.9 MiB
0
809155 
1
 
39256

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters848411
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0809155
95.4%
139256
 
4.6%

Length

2022-05-07T12:35:40.039166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T12:35:40.243984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0809155
95.4%
139256
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0809155
95.4%
139256
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number848411
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0809155
95.4%
139256
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common848411
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0809155
95.4%
139256
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII848411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0809155
95.4%
139256
 
4.6%

BucketAttendees
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04579384284
Minimum0
Maximum6
Zeros818422
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size6.5 MiB
2022-05-07T12:35:40.377051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2730726726
Coefficient of variation (CV)5.963087079
Kurtosis84.23380299
Mean0.04579384284
Median Absolute Deviation (MAD)0
Skewness8.11346996
Sum38852
Variance0.0745686845
MonotonicityNot monotonic
2022-05-07T12:35:40.537250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0818422
96.5%
124428
 
2.9%
23109
 
0.4%
31676
 
0.2%
4709
 
0.1%
560
 
< 0.1%
67
 
< 0.1%
ValueCountFrequency (%)
0818422
96.5%
124428
 
2.9%
23109
 
0.4%
31676
 
0.2%
4709
 
0.1%
560
 
< 0.1%
67
 
< 0.1%
ValueCountFrequency (%)
67
 
< 0.1%
560
 
< 0.1%
4709
 
0.1%
31676
 
0.2%
23109
 
0.4%
124428
 
2.9%
0818422
96.5%

dwpt
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2806
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.12271461
Minimum-22.8
Maximum23
Zeros1878
Zeros (%)0.2%
Negative128952
Negative (%)15.2%
Memory size6.5 MiB
2022-05-07T12:35:40.717419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-22.8
5-th percentile-3.3
Q11.7
median6.15
Q310.85
95-th percentile15.6
Maximum23
Range45.8
Interquartile range (IQR)9.15

Descriptive statistics

Standard deviation6.114669039
Coefficient of variation (CV)0.9986859471
Kurtosis0.02577060591
Mean6.12271461
Median Absolute Deviation (MAD)4.55
Skewness-0.2833830907
Sum5194578.425
Variance37.38917746
MonotonicityNot monotonic
2022-05-07T12:35:40.923399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.52768
 
0.3%
1.92662
 
0.3%
1.42656
 
0.3%
2.12629
 
0.3%
22617
 
0.3%
1.12617
 
0.3%
0.72593
 
0.3%
8.32536
 
0.3%
5.12521
 
0.3%
3.52515
 
0.3%
Other values (2796)822297
96.9%
ValueCountFrequency (%)
-22.86
< 0.1%
-22.756
< 0.1%
-22.76
< 0.1%
-22.656
< 0.1%
-22.66
< 0.1%
-22.56
< 0.1%
-22.3256
< 0.1%
-22.26
< 0.1%
-22.056
< 0.1%
-226
< 0.1%
ValueCountFrequency (%)
236
< 0.1%
22.5256
< 0.1%
22.46
< 0.1%
22.056
< 0.1%
21.812
< 0.1%
21.6256
< 0.1%
21.5756
< 0.1%
21.5756
< 0.1%
21.456
< 0.1%
21.356
< 0.1%

rhum
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct330
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.09964186
Minimum0
Maximum100
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.5 MiB
2022-05-07T12:35:41.145917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.5
Q167
median81.5
Q390.75
95-th percentile98
Maximum100
Range100
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation17.41226757
Coefficient of variation (CV)0.2258410954
Kurtosis-0.009567350005
Mean77.09964186
Median Absolute Deviation (MAD)11
Skewness-0.8783361371
Sum65412184.25
Variance303.187062
MonotonicityNot monotonic
2022-05-07T12:35:41.344929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9012188
 
1.4%
9812137
 
1.4%
9311856
 
1.4%
9511845
 
1.4%
10011826
 
1.4%
9611787
 
1.4%
8911717
 
1.4%
8411712
 
1.4%
9111648
 
1.4%
9211617
 
1.4%
Other values (320)730078
86.1%
ValueCountFrequency (%)
018
 
< 0.1%
176
 
< 0.1%
1842
< 0.1%
18.256
 
< 0.1%
18.518
 
< 0.1%
18.756
 
< 0.1%
1978
< 0.1%
19.524
 
< 0.1%
19.7512
 
< 0.1%
2042
< 0.1%
ValueCountFrequency (%)
10011826
1.4%
99.752022
 
0.2%
99.52586
 
0.3%
99.252190
 
0.3%
9911148
1.3%
98.752528
 
0.3%
98.54172
 
0.5%
98.252864
 
0.3%
9812137
1.4%
97.752611
 
0.3%

prcp
Real number (ℝ≥0)

ZEROS

Distinct470
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07265720859
Minimum0
Maximum26.2
Zeros734300
Zeros (%)86.6%
Negative0
Negative (%)0.0%
Memory size6.5 MiB
2022-05-07T12:35:41.572180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.375
Maximum26.2
Range26.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4150709894
Coefficient of variation (CV)5.712729644
Kurtosis563.5979352
Mean0.07265720859
Median Absolute Deviation (MAD)0
Skewness17.65664282
Sum61643.175
Variance0.1722839262
MonotonicityNot monotonic
2022-05-07T12:35:41.801642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0734300
86.6%
0.113368
 
1.6%
0.058305
 
1.0%
0.0757609
 
0.9%
0.26362
 
0.7%
0.154444
 
0.5%
0.0253550
 
0.4%
0.33450
 
0.4%
0.43306
 
0.4%
0.0253260
 
0.4%
Other values (460)60457
 
7.1%
ValueCountFrequency (%)
0734300
86.6%
0.0253550
 
0.4%
0.0253260
 
0.4%
0.051527
 
0.2%
0.058305
 
1.0%
0.075721
 
0.1%
0.0757609
 
0.9%
0.1457
 
0.1%
0.113368
 
1.6%
0.1251960
 
0.2%
ValueCountFrequency (%)
26.26
< 0.1%
21.86
< 0.1%
21.156
< 0.1%
20.2256
< 0.1%
18.56
< 0.1%
17.56
< 0.1%
176
< 0.1%
16.56
< 0.1%
16.46
< 0.1%
16.16
< 0.1%

wspd
Real number (ℝ≥0)

Distinct2182
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.03301996
Minimum0
Maximum54
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.5 MiB
2022-05-07T12:35:42.230019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.625
Q17.125
median10.95
Q315.725
95-th percentile24.3
Maximum54
Range54
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation6.444632637
Coefficient of variation (CV)0.5355789865
Kurtosis0.9798285413
Mean12.03301996
Median Absolute Deviation (MAD)4.15
Skewness0.9269693532
Sum10208946.5
Variance41.53328983
MonotonicityNot monotonic
2022-05-07T12:35:42.490631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.48198
 
1.0%
98077
 
1.0%
10.87957
 
0.9%
7.27936
 
0.9%
6.17928
 
0.9%
6.57704
 
0.9%
8.37481
 
0.9%
9.77473
 
0.9%
7.67218
 
0.9%
10.17057
 
0.8%
Other values (2172)771382
90.9%
ValueCountFrequency (%)
018
 
< 0.1%
0.454
 
< 0.1%
0.47524
 
< 0.1%
0.5524
 
< 0.1%
0.62524
 
< 0.1%
0.7162
< 0.1%
0.7512
 
< 0.1%
0.830
 
< 0.1%
0.856
 
< 0.1%
0.87548
 
< 0.1%
ValueCountFrequency (%)
546
< 0.1%
52.3756
< 0.1%
52.212
< 0.1%
51.46
< 0.1%
51.1256
< 0.1%
50.756
< 0.1%
50.66
< 0.1%
50.46
< 0.1%
50.056
< 0.1%
49.86
< 0.1%

tsun
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct281
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.09812726
Minimum0
Maximum60
Zeros568344
Zeros (%)67.0%
Negative0
Negative (%)0.0%
Memory size6.5 MiB
2022-05-07T12:35:42.720728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310.75
95-th percentile60
Maximum60
Range60
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation20.33364974
Coefficient of variation (CV)1.832169452
Kurtosis0.9171284956
Mean11.09812726
Median Absolute Deviation (MAD)0
Skewness1.601715103
Sum9415773.25
Variance413.4573119
MonotonicityNot monotonic
2022-05-07T12:35:42.908119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0568344
67.0%
6063656
 
7.5%
15070
 
0.6%
0.53738
 
0.4%
33707
 
0.4%
1.53542
 
0.4%
23304
 
0.4%
0.753294
 
0.4%
592938
 
0.3%
62647
 
0.3%
Other values (271)188171
 
22.2%
ValueCountFrequency (%)
0568344
67.0%
0.252208
 
0.3%
0.3756
 
< 0.1%
0.53738
 
0.4%
0.753294
 
0.4%
15070
 
0.6%
1.1256
 
< 0.1%
1.25836
 
0.1%
1.4166666676
 
< 0.1%
1.53542
 
0.4%
ValueCountFrequency (%)
6063656
7.5%
59.751161
 
0.1%
59.51750
 
0.2%
59.251610
 
0.2%
59.1256
 
< 0.1%
592938
 
0.3%
58.75511
 
0.1%
58.51516
 
0.2%
58.25546
 
0.1%
581954
 
0.2%

coco
Real number (ℝ≥0)

ZEROS

Distinct147
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.351226292
Minimum0
Maximum25
Zeros15378
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size6.5 MiB
2022-05-07T12:35:43.171110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.25
median4
Q36
95-th percentile8
Maximum25
Range25
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation2.940262781
Coefficient of variation (CV)0.6757319854
Kurtosis4.024227738
Mean4.351226292
Median Absolute Deviation (MAD)2
Skewness1.46220686
Sum3691628.25
Variance8.645145224
MonotonicityNot monotonic
2022-05-07T12:35:43.537131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4265065
31.2%
1136257
16.1%
893397
 
11.0%
743759
 
5.2%
333720
 
4.0%
526974
 
3.2%
225616
 
3.0%
2.520264
 
2.4%
3.517872
 
2.1%
3.2517666
 
2.1%
Other values (137)167821
19.8%
ValueCountFrequency (%)
015378
 
1.8%
1136257
16.1%
1.08333333318
 
< 0.1%
1.0833333336
 
< 0.1%
1.12518
 
< 0.1%
1.16666666724
 
< 0.1%
1.256573
 
0.8%
1.33333333318
 
< 0.1%
1.3333333336
 
< 0.1%
1.37530
 
< 0.1%
ValueCountFrequency (%)
25360
< 0.1%
23.2518
 
< 0.1%
236
 
< 0.1%
21.518
 
< 0.1%
21738
0.1%
20.756
 
< 0.1%
20.2512
 
< 0.1%
19.7524
 
< 0.1%
19.524
 
< 0.1%
19.2524
 
< 0.1%

Interactions

2022-05-07T12:35:16.953088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:31:49.978116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:01.216208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:12.540873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:23.099215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:32.970946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:42.600446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:52.296790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:01.732129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:13.476407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:24.697912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:34.860962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:44.074617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:52.762835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:01.512987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:10.009447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:19.111743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:28.058857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:37.934988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:47.835395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:58.830659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:07.837682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:17.371451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:31:50.607075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:01.763705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:13.084130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:23.943654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:33.423074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:43.034507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:52.711582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:02.151124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:14.141551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:25.116762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:35.608884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:44.485855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:53.152957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:01.902384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:10.374531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:19.533366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:28.455724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:38.602915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:48.297214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:59.232076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:08.210190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:18.063417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:31:51.264803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:02.297596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:13.677582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:24.399285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:33.881416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:43.461716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:53.151474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:02.592994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:14.628779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:25.546000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:36.038402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:44.880503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:53.558605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:02.297409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:11.098895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:19.912920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:28.869008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:39.316579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:49.006495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:59.657218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:08.642554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:18.443439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:31:51.779127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:02.744732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:14.148338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:24.841871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:34.303242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:43.877013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:53.574885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:02.994701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:15.049477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:25.962869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:36.443966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:45.251175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:53.946286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:02.678381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:11.623080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:20.266384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:29.231672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:39.806281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:49.612060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:00.046296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:09.025888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:18.835697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:31:52.229344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:03.187362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:14.658121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:25.305303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:34.731347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:44.299709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:53.978708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:03.413134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:15.499970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:26.385787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-05-07T12:34:06.539648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:15.545621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:24.493703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:32.958905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:43.841211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:55.301176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:03.964126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:13.299206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:22.746919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:31:57.420014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:08.894907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:19.815492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:29.541059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:39.223564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:48.670188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:58.244618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:08.340412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:21.106560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:30.465046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:40.769458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:49.609604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:58.095232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:06.936073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:15.934561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:24.895557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:33.342705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:44.310003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:55.729451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:04.358480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:13.720886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:23.163552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:31:57.911953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:09.359422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:20.348341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:29.967795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:39.662317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:49.117648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:58.681818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:08.776045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:21.587907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:31.053616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:41.179053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:49.994548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:58.509417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:07.337994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:16.333555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:25.310925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:33.756375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:44.825260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:56.133431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:05.079816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:14.155297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:23.589007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:31:58.389274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:09.829220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:20.751053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:30.390481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:40.093836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:49.562853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:59.120441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:09.228865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:22.024629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:31.655982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:41.617353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:50.423049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:58.922189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:07.749223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:16.750787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:25.743955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:34.162988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:45.258750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:56.518727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:05.437734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:14.594362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:23.969951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:31:58.836700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:10.279768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:21.141900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:30.827464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:40.520690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:49.959386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:59.577347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:09.885945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:22.451977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:32.061819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:42.023411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:50.825382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:59.531163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:08.138883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:17.143773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:26.124419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:34.634876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:45.674290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:56.898303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:05.816936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:14.991238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:24.367112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:31:59.346076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:10.770313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:21.526621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:31.249468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:40.915932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:50.383459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:59.992651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:10.740271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:22.871176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:32.496267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:42.424157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:51.210152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:59.911529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:08.529934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:17.535900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:26.514085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:35.167200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:46.098936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:57.282317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:06.208492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:15.381605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:24.755943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:31:59.817549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:11.213158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:21.915696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:31.675085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:41.337048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:51.010042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:00.413050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:11.556437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:23.299134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:32.931856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:42.831399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:51.624742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:00.311220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:08.929882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:17.919405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:26.912802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:36.288015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:46.530236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:57.668746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:06.593475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:15.781466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:25.167948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:00.283553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:11.669478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:22.322677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:32.100984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:41.758108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:51.428097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:00.865321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:12.210106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:23.823477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:33.467095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:43.262864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:52.001468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:00.719099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:09.330025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:18.299553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:27.289245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:36.813600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:46.950949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:58.048059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:07.003119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:16.163346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:25.546352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:00.756287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:12.104326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:22.724444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:32.523598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:42.182648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:32:51.870824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:01.290986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:12.844866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:24.241928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:34.094832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:43.662700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:33:52.377458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:01.114536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:09.687916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:18.694342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:27.677339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:37.384765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:47.375994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:34:58.434442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:07.398622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T12:35:16.563044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-07T12:35:43.947786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-07T12:35:44.756549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-07T12:35:45.454581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-07T12:35:45.966240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-07T12:35:25.918982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-07T12:35:28.292417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexRoomRoomTemperatureAirqualityPercHeaterPercCoolerPercTempSupplyAirRelativeHumiditySupplyAirHeatingPowerCoolingPowerAirTemperatureWindDirectionBrightnessNorthBrightnessEastBrightnessSouthBrightnessWestBeamerstateBucketAttendeesdwptrhumprcpwspdtsuncoco
02018-01-01 00:00:000.021.2999990.00.00.018.4040.7999990.01.49.52350.00.00.00.000.06.581.00.022.30.00.0
12018-01-01 00:00:001.021.6000000.00.00.018.4040.7999990.01.49.52350.00.00.00.000.06.581.00.022.30.00.0
22018-01-01 00:00:002.021.7999990.00.00.018.4040.7999990.01.49.52350.00.00.00.000.06.581.00.022.30.00.0
32018-01-01 00:00:003.021.9000000.00.00.018.4040.7999990.01.49.52350.00.00.00.000.06.581.00.022.30.00.0
42018-01-01 00:00:004.020.4000000.00.00.018.4040.7999990.01.49.52350.00.00.00.000.06.581.00.022.30.00.0
52018-01-01 00:00:005.021.9000000.00.00.018.4040.7999990.01.49.52350.00.00.00.000.06.581.00.022.30.00.0
62018-01-01 00:15:000.021.2999990.00.00.018.3340.7999990.00.59.52530.00.00.00.000.06.380.00.022.40.00.0
72018-01-01 00:15:001.021.6000000.00.00.018.3340.7999990.00.59.52530.00.00.00.000.06.380.00.022.40.00.0
82018-01-01 00:15:002.021.9000000.00.00.018.3340.7999990.00.59.52530.00.00.00.000.06.380.00.022.40.00.0
92018-01-01 00:15:003.021.9000000.00.00.018.3340.7999990.00.59.52530.00.00.00.000.06.380.00.022.40.00.0

Last rows

df_indexRoomRoomTemperatureAirqualityPercHeaterPercCoolerPercTempSupplyAirRelativeHumiditySupplyAirHeatingPowerCoolingPowerAirTemperatureWindDirectionBrightnessNorthBrightnessEastBrightnessSouthBrightnessWestBeamerstateBucketAttendeesdwptrhumprcpwspdtsuncoco
8484012021-12-31 23:30:002.019.60000023.7000011.40.917.53000138.0000000.00.212.92700.00.00.00.010.00.00.00.00.00.00.0
8484022021-12-31 23:30:003.019.0000009.0000001.40.917.53000138.0000000.00.212.92700.00.00.00.000.00.00.00.00.00.00.0
8484032021-12-31 23:30:004.018.29999920.2000011.40.917.53000138.0000000.00.212.92700.00.00.00.000.00.00.00.00.00.00.0
8484042021-12-31 23:30:005.018.79999925.1000001.40.917.53000138.0000000.00.212.92700.00.00.00.000.00.00.00.00.00.00.0
8484052021-12-31 23:45:000.018.50000016.7999991.30.917.59000038.0999980.00.212.73150.00.00.00.000.00.00.00.00.00.00.0
8484062021-12-31 23:45:001.019.40000021.0000001.30.917.59000038.0999980.00.212.73150.00.00.00.000.00.00.00.00.00.00.0
8484072021-12-31 23:45:002.019.60000023.6000001.30.917.59000038.0999980.00.212.73150.00.00.00.010.00.00.00.00.00.00.0
8484082021-12-31 23:45:003.019.0000009.2000001.30.917.59000038.0999980.00.212.73150.00.00.00.000.00.00.00.00.00.00.0
8484092021-12-31 23:45:004.018.29999920.2000011.30.917.59000038.0999980.00.212.73150.00.00.00.000.00.00.00.00.00.00.0
8484102021-12-31 23:45:005.018.90000025.2000011.30.917.59000038.0999980.00.212.73150.00.00.00.000.00.00.00.00.00.00.0

Duplicate rows

Most frequently occurring

df_indexRoomRoomTemperatureAirqualityPercHeaterPercCoolerPercTempSupplyAirRelativeHumiditySupplyAirHeatingPowerCoolingPowerAirTemperatureWindDirectionBrightnessNorthBrightnessEastBrightnessSouthBrightnessWestBeamerstateBucketAttendeesdwptrhumprcpwspdtsuncoco# duplicates
962018-10-28 02:30:004.020.90.00.00.019.4842.0000000.00.03.600000860.00.00.00.000.01.85089.500.012.40.04.064
972018-10-28 02:30:004.020.90.00.00.019.4842.0000000.00.04.00000000.00.00.00.000.01.85089.500.012.40.04.064
982018-10-28 02:30:005.022.60.00.00.019.4842.0000000.00.03.600000860.00.00.00.000.01.85089.500.012.40.04.064
992018-10-28 02:30:005.022.60.00.00.019.4842.0000000.00.04.00000000.00.00.00.000.01.85089.500.012.40.04.064
1242018-10-28 02:45:004.020.90.00.00.019.4842.0000000.00.03.600000610.00.00.00.000.01.82589.250.012.50.04.064
1252018-10-28 02:45:004.020.90.00.00.019.4842.0000000.00.04.0000003600.00.00.00.000.01.82589.250.012.50.04.064
1262018-10-28 02:45:005.022.60.00.00.019.4842.0000000.00.03.600000610.00.00.00.000.01.82589.250.012.50.04.064
1272018-10-28 02:45:005.022.60.00.00.019.4842.0000000.00.04.0000003600.00.00.00.000.01.82589.250.012.50.04.064
1442019-10-27 02:00:004.021.90.01.60.020.0142.7000010.00.016.2000012070.00.00.00.000.010.50067.000.024.10.04.064
1452019-10-27 02:00:004.021.90.01.60.020.0142.7000010.00.017.4000002690.00.00.00.000.010.50067.000.024.10.04.064